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Clonal evolution in breast cancer revealed by single nucleus genome sequencing

Author

Listed:
  • Yong Wang

    (The University of Texas MD Anderson Cancer Center)

  • Jill Waters

    (The University of Texas MD Anderson Cancer Center)

  • Marco L. Leung

    (The University of Texas MD Anderson Cancer Center
    The University of Texas Graduate School of Biomedical Sciences)

  • Anna Unruh

    (The University of Texas MD Anderson Cancer Center)

  • Whijae Roh

    (The University of Texas MD Anderson Cancer Center)

  • Xiuqing Shi

    (The University of Texas MD Anderson Cancer Center)

  • Ken Chen

    (The University of Texas MD Anderson Cancer Center)

  • Paul Scheet

    (The University of Texas Graduate School of Biomedical Sciences
    The University of Texas MD Anderson Cancer Center)

  • Selina Vattathil

    (The University of Texas Graduate School of Biomedical Sciences
    The University of Texas MD Anderson Cancer Center)

  • Han Liang

    (The University of Texas MD Anderson Cancer Center)

  • Asha Multani

    (The University of Texas MD Anderson Cancer Center)

  • Hong Zhang

    (The University of Texas MD Anderson Cancer Center)

  • Rui Zhao

    (Dana-Farber Cancer Institute, Harvard School of Public Health)

  • Franziska Michor

    (Dana-Farber Cancer Institute, Harvard School of Public Health)

  • Funda Meric-Bernstam

    (The University of Texas MD Anderson Cancer Center Department of Investigational Cancer Therapeutics)

  • Nicholas E. Navin

    (The University of Texas MD Anderson Cancer Center
    The University of Texas Graduate School of Biomedical Sciences
    The University of Texas MD Anderson Cancer Center)

Abstract

Sequencing studies of breast tumour cohorts have identified many prevalent mutations, but provide limited insight into the genomic diversity within tumours. Here we developed a whole-genome and exome single cell sequencing approach called nuc-seq that uses G2/M nuclei to achieve 91% mean coverage breadth. We applied this method to sequence single normal and tumour nuclei from an oestrogen-receptor-positive (ER+) breast cancer and a triple-negative ductal carcinoma. In parallel, we performed single nuclei copy number profiling. Our data show that aneuploid rearrangements occurred early in tumour evolution and remained highly stable as the tumour masses clonally expanded. In contrast, point mutations evolved gradually, generating extensive clonal diversity. Using targeted single-molecule sequencing, many of the diverse mutations were shown to occur at low frequencies (

Suggested Citation

  • Yong Wang & Jill Waters & Marco L. Leung & Anna Unruh & Whijae Roh & Xiuqing Shi & Ken Chen & Paul Scheet & Selina Vattathil & Han Liang & Asha Multani & Hong Zhang & Rui Zhao & Franziska Michor & Fun, 2014. "Clonal evolution in breast cancer revealed by single nucleus genome sequencing," Nature, Nature, vol. 512(7513), pages 155-160, August.
  • Handle: RePEc:nat:nature:v:512:y:2014:i:7513:d:10.1038_nature13600
    DOI: 10.1038/nature13600
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    Cited by:

    1. Wei Sun & Chong Jin & Jonathan A. Gelfond & Ming‐Hui Chen & Joseph G. Ibrahim, 2020. "Joint analysis of single‐cell and bulk tissue sequencing data to infer intratumor heterogeneity," Biometrics, The International Biometric Society, vol. 76(3), pages 983-994, September.
    2. Jinhyun Kim & Sungsik Kim & Huiran Yeom & Seo Woo Song & Kyoungseob Shin & Sangwook Bae & Han Suk Ryu & Ji Young Kim & Ahyoun Choi & Sumin Lee & Taehoon Ryu & Yeongjae Choi & Hamin Kim & Okju Kim & Yu, 2023. "Barcoded multiple displacement amplification for high coverage sequencing in spatial genomics," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    3. Jae-Woong Min & Woo Jin Kim & Jeong A Han & Yu-Jin Jung & Kyu-Tae Kim & Woong-Yang Park & Hae-Ock Lee & Sun Shim Choi, 2015. "Identification of Distinct Tumor Subpopulations in Lung Adenocarcinoma via Single-Cell RNA-seq," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-17, August.
    4. Xiaodong Liu & Ke Zhang & Neslihan A. Kaya & Zhe Jia & Dafei Wu & Tingting Chen & Zhiyuan Liu & Sinan Zhu & Axel M. Hillmer & Torsten Wuestefeld & Jin Liu & Yun Shen Chan & Zheng Hu & Liang Ma & Li Ji, 2024. "Tumor phylogeography reveals block-shaped spatial heterogeneity and the mode of evolution in Hepatocellular Carcinoma," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    5. Humberto Contreras-Trujillo & Jiya Eerdeng & Samir Akre & Du Jiang & Jorge Contreras & Basia Gala & Mary C. Vergel-Rodriguez & Yeachan Lee & Aparna Jorapur & Areen Andreasian & Lisa Harton & Charles S, 2021. "Deciphering intratumoral heterogeneity using integrated clonal tracking and single-cell transcriptome analyses," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    6. Zhengcheng He & Ryan Ghorayeb & Susanna Tan & Ke Chen & Amanda C. Lorentzian & Jack Bottyan & Syed Mohammed Musheer Aalam & Miguel Angel Pujana & Philipp F. Lange & Nagarajan Kannan & Connie J. Eaves , 2022. "Pathogenic BRCA1 variants disrupt PLK1-regulation of mitotic spindle orientation," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    7. Xian F Mallory & Mohammadamin Edrisi & Nicholas Navin & Luay Nakhleh, 2020. "Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-24, July.
    8. Cheng-Kai Shiau & Lina Lu & Rachel Kieser & Kazutaka Fukumura & Timothy Pan & Hsiao-Yun Lin & Jie Yang & Eric L. Tong & GaHyun Lee & Yuanqing Yan & Jason T. Huse & Ruli Gao, 2023. "High throughput single cell long-read sequencing analyses of same-cell genotypes and phenotypes in human tumors," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    9. David Lähnemann & Johannes Köster & Ute Fischer & Arndt Borkhardt & Alice C. McHardy & Alexander Schönhuth, 2021. "Accurate and scalable variant calling from single cell DNA sequencing data with ProSolo," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    10. Thanh Nguyen & Asim Bhatti & Samuel Yang & Saeid Nahavandi, 2016. "RNA-Seq Count Data Modelling by Grey Relational Analysis and Nonparametric Gaussian Process," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-18, October.

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